A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models

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Authors: Zhengqing Zang, Yuqi Ding, Yanmei Gu, Changkai Song, Zhengkai Yang, Guoping Du, Junbo Zhao, Haobo Wang
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Abstract

Human logic has gradually shifted from intuition-driven inference to rigorous formal systems. Motivated by recent advances in large language models (LLMs), we explore whether LLMs exhibit a similar evolution in the underlying logical framework. Using existential import as a probe, we for evaluate syllogism under traditional and modern logic. Through extensive experiments of testing SOTA LLMs on a new syllogism dataset, we have some interesting findings: (i) Model size scaling promotes the shift toward modern logic; (ii) Thinking serves as an efficient accelerator beyond parameter scaling; (iii) the Base model plays a crucial role in determining how easily and stably this shift can emerge. Beyond these core factors, we conduct additional experiments for in-depth analysis of properties of current LLMs on syllogistic reasoning.

Fields of Study & Subjects

Logic Reasoning and Knowledge Topic Modeling Natural Language Processing Techniques

Publication details

Journal / source
arXiv (Cornell University)
Publication date
24 January 2026
Document type
Preprint